Stacked Ensemble Method: An Advanced Machine Learning Approach for Anomaly-based Intrusion Detection System

Abstract

The subject of this article is IDS-Intrusion Detection Systems, which are strongly related to a comprehensive cyber attack prevention system. In the present day, an IDS for network infrastructure is a crucial topic. The advancement of SDN-Software Defined Networking has led to a rising need for software-based IDS-Intrusion Detection Systems. Diverse methodologies, including machine learning algorithms and other statistical models, have been used to develop distinct kinds of IDS-Intrusion Detection Systems to enhance performance. But still, that needs to be improved. Several studies have focused on solving these problems for this reason, utilizing methods like conventional machine learning models. However, existing systems need to improve, including low detection rate and high false alarm rate. The aim is to improve performance, specifically in terms of increases in detection rate. This work introduces a new IDS-Intrusion Detection System named SIDS-Stacked Intrusion Detection System, which utilizes a stack-based approach to improve detection accuracy and resilience. The objective is to utilize various predictive algorithms most efficiently. An ensemble classifier method is used to enhance the precision of the final prediction by amalgamating the outputs of multiple models. This research implemented numerous ML-machine learning methodologies, including Stochastic Gradient Descent, Logistic Regression, Random Forest, and Deep Neural Networks, to construct a multilayered model that would optimize network intrusion detection accuracy. This challenging research project employs the NSL-KDD dataset. In previous studies, the stacked model (DNN1 + DNN2) has a maximum accuracy of 97.90% for intrusion detection. However, the suggested trained model outperforms existing models by 98.40%. Additionally, the offered stacked model attains F1-score 99.2%, a FPR-false positive rate 95.6%, and a FNR-false negative rate 1.42%. In conclusion, the findings indicate that a stacked ensemble method can enhance evaluation metrics and provide more consistent performance.